it keeps on going

import numpy as np
from scipy.stats import wilcoxon


def array_to_percentages(l):
  l_sum = 0
  for i in l:
    l_sum += i

  return (np.true_divide(l[0], l_sum) * 100.0, np.true_divide(l[1], l_sum) * 100.0, np.true_divide(l[2], l_sum) * 100.0, np.true_divide(l[3], l_sum) * 100.0, np.true_divide(l[4], l_sum) * 100.0)

c1_f_comfort = [0,3,3,14,14]
c1_s_comfort = [8,16,6,3,1]
c2_f_comfort = [2,4,3,16,9]
c2_s_comfort = [18,8,2,4,2]
c3_f_comfort = [0,2,5,15,12]
c3_s_comfort = [12,11,7,3,1]
c4_f_comfort = [0,4,5,14,11]
c4_s_comfort = [16,7,6,2,3]

c1_friend = [3,1,4,4,4,4,4,2,3,4,3,3,1,3,4,3,1,3,3,4,3,4,4,2,3,4,3,3,4,3,3,4,2,4]
c1_stranger = [2,0,0,4,1,1,3,0,2,0,1,3,0,1,2,1,0,1,1,2,1,2,2,1,1,1,1,0,0,1,3,1,1,1]
c2_friend = [3,3,3,4,4,3,4,1,3,3,3,3,1,2,4,3,0,3,4,4,3,4,3,1,2,1,3,3,4,3,3,4,2,0]
c2_stranger = [0,1,0,4,1,1,3,0,1,3,0,3,0,0,2,0,0,0,0,1,0,1,2,0,0,0,0,4,0,1,3,0,1,0]
c3_friend = [3,3,4,4,4,3,4,2,3,2,4,3,1,3,4,3,3,2,4,3,3,4,3,2,4,1,3,3,4,3,3,4,2,4]
c3_stranger = [1,0,0,4,0,0,3,1,1,2,1,3,0,0,2,1,2,0,2,1,0,2,2,1,0,0,1,0,1,0,3,2,1,1]
c4_friend = [3,3,4,4,4,3,4,1,3,2,3,3,1,3,4,3,2,3,4,4,3,4,3,2,3,1,4,2,4,4,3,3,2,1]
c4_stranger = [0,0,0,4,0,0,4,0,1,2,1,3,0,1,2,0,0,0,1,2,0,1,2,1,0,0,0,4,2,0,3,0,1,2]

test_dict = {}
test_dict["c1_friend"] = [3, 1, 4, 4, 4, 4, 4, 2, 3, 4, 3, 3, 1, 3, 4, 3, 1, 3, 3, 4, 3, 4, 4 ,2, 3, 4, 3, 3, 4, 3, 3, 4, 2, 4]
test_dict["c1_stranger"] = [2, 0, 0, 4, 1, 1, 3, 0, 2, 0, 1, 3, 0, 1, 2, 1, 0, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 0, 0, 1, 3, 1, 1, 1]
test_dict["c2_friend"] = [3,3,3,4,4,3,4,1,3,3,3,3,1,2,4,3,0,3,4,4,3,4,3,1,2,1,3,3,4,3,3,4,2,0]
test_dict["c2_stranger"] = [0,1,0,4,1,1,3,0,1,3,0,3,0,0,2,0,0,0,0,1,0,1,2,0,0,0,0,4,0,1,3,0,1,0]
test_dict["c3_friend"] = [3,3,4,4,4,3,4,2,3,2,4,3,1,3,4,3,3,2,4,3,3,4,3,2,4,1,3,3,4,3,3,4,2,4]
test_dict["c3_stranger"] = [1,0,0,4,0,0,3,1,1,2,1,3,0,0,2,1,2,0,2,1,0,2,2,1,0,0,1,0,1,0,3,2,1,1]
test_dict["c4_friend"] = [3,3,4,4,4,3,4,1,3,2,3,3,1,3,4,3,2,3,4,4,3,4,3,2,3,1,4,2,4,4,3,3,2,1]
test_dict["c4_stranger"] = [0,0,0,4,0,0,4,0,1,2,1,3,0,1,2,0,0,0,1,2,0,1,2,1,0,0,0,4,2,0,3,0,1,2]

count_dict = {}
count_dict["c1_friend"] = [0,3,3,14,14]
count_dict["c1_stranger"] = [8,16,6,3,1]
count_dict["c2_friend"] = [2,4,3,16,9]
count_dict["c2_stranger"] = [18,8,2,4,2]
count_dict["c3_friend"] = [0,2,5,15,12]
count_dict["c3_stranger"] = [12,11,7,3,1]
count_dict["c4_friend"] = [0,4,5,14,11]
count_dict["c4_stranger"] = [16,7,6,2,3]

key_len = len( list(test_dict.keys()) )

# String slice to get the desired results
# Compare the samples
i = 0

while i < key_len:
    t = list(test_dict.keys())[i]

    for k in list(test_dict.keys())[i+1:]:
        stat, p = wilcoxon(test_dict[t], test_dict[k])

        if p < 0.05:
            print(t + "  " + k)
            print('Statistics=%f, p=%f' % (stat, p))
            print("")

    i += 1

"""
import numpy as np
import matplotlib.pyplot as plt

N = 5
c1_f_comfort = (0,3,3,14,14)
c1_s_comfort = (8,16,6,3,1)

ind = np.arange(N)      # the x locations for the groups
width = 0.35            # the width of the bars

fig, ax = plt.subplots()
rects1 = ax.bar(ind, c1_f_comfort, width, color='r')

rects2 = ax.bar(ind + width, c1_s_comfort, width, color='b')

# add some text for labels, title and axes ticks
ax.set_ylabel('Comfort Level')
ax.set_title('Context 1 Comfort Count Comparison')
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(('Very Un', 'Un', 'Neu', 'Com', 'Very Com'))

ax.legend((rects1[0], rects2[0]), ('Friend', 'Stranger'))


def autolabel(rects):
   # Attach a text label above each bar displaying its height
   for rect in rects:
       height = rect.get_height()
       ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,
               '%d' % int(height),
               ha='center', va='bottom')

autolabel(rects1)
autolabel(rects2)

plt.show()
"""


"""
# libraries
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
import pandas as pd

# Data
r = [0]

raw_data = {'greenBars': [20], 'orangeBars': [20],'blueBars': [20], 'purpleBars' : [20], 'greyBars' : [20]}

df = pd.DataFrame(raw_data)

# From raw value to percentage
totals = [i+j+k+x+y for i,j,k,x,y in zip(df['greenBars'], df['orangeBars'], df['blueBars'], df['purpleBars'], df['greyBars'])]

greenBars = [i / j * 100 for i,j in zip(df['greenBars'], totals)]
orangeBars = [i / j * 100 for i,j in zip(df['orangeBars'], totals)]
blueBars = [i / j * 100 for i,j in zip(df['blueBars'], totals)]

# #A569BD
purpleBars = [i / j * 100 for i,j in zip(df['purpleBars'], totals)]

# #5D6D7E
greyBars = [i / j * 100 for i,j in zip(df['greyBars'], totals)]

# plot
barWidth = 0.5
names = ('A')

# Create green Bars
plt.bar(r, greenBars, color='#b5ffb9', edgecolor='white', width=barWidth, label="group A")

# Create orange Bars
plt.bar(r, orangeBars, bottom=greenBars, color='#f9bc86', edgecolor='white', width=barWidth, label="group B")

# Create blue Bars
plt.bar(r, blueBars, bottom=[i+j for i,j in zip(greenBars, orangeBars)], color='#a3acff', edgecolor='white', width=barWidth, label="group C")

# Create purple Bars
plt.bar(r, purpleBars, bottom=[i+j+k for i,j,k in zip(blueBars, greenBars, orangeBars)], color='#a569bd', edgecolor='white', width=barWidth, label="group D")

# Create grey Bars
plt.bar(r, greyBars, bottom=[i+j+k+l for i,j,k,l in zip(purpleBars, blueBars, greenBars, orangeBars)], color='#5d6d7e', edgecolor='white', width=barWidth, label="group E")

# Custom x axis
plt.xticks(r, names)
plt.xlabel("group")

# Show graphic
plt.show()
"""


import numpy as np
import matplotlib.pyplot as plt
# import matplotlib.axes as axes

x_labels = ['C1F', 'C1S']

ind = [0, 1]

c1_f_percentages = array_to_percentages(c1_f_comfort)
c1_s_percentages = array_to_percentages(c1_s_comfort)

print(c1_f_percentages)
print(c1_s_percentages)

plt.box(on=None)

plt.bar(ind[0], c1_f_percentages[4], width=0.3, label='1', color='green', bottom=c1_f_percentages[3]+c1_f_percentages[2]+c1_f_percentages[1]+c1_f_percentages[0])
plt.bar(ind[0], c1_f_percentages[3], width=0.3, label='2', color='blue', bottom=c1_f_percentages[2]+c1_f_percentages[1]+c1_f_percentages[0])
plt.bar(ind[0], c1_f_percentages[2], width=0.3, label='3', color='#5d6d7e', bottom=c1_f_percentages[1]+c1_f_percentages[0])
plt.bar(ind[0], c1_f_percentages[1], width=0.3, label='4', color='#a569bd', bottom=c1_f_percentages[0])
plt.bar(ind[0], c1_f_percentages[0], width=0.3, label='5', color='#a3acff')

plt.bar(ind[1], c1_s_percentages[4], width=0.3, label='1', color='green', bottom=c1_s_percentages[3]+c1_s_percentages[2]+c1_s_percentages[1]+c1_s_percentages[0])
plt.bar(ind[1], c1_s_percentages[3], width=0.3, label='2', color='blue', bottom=c1_s_percentages[2]+c1_s_percentages[1]+c1_s_percentages[0])
plt.bar(ind[1], c1_s_percentages[2], width=0.3, label='3', color='#5d6d7e', bottom=c1_s_percentages[1]+c1_s_percentages[0])
plt.bar(ind[1], c1_s_percentages[1], width=0.3, label='4', color='#a569bd', bottom=c1_s_percentages[0])
plt.bar(ind[1], c1_s_percentages[0], width=0.3, label='5', color='#a3acff')

plt.grid(axis='y')

ax = plt.axes()

ax.set_axisbelow(True)

plt.xticks(ind, x_labels)
plt.ylabel("Y LABEL")
plt.ylim=1.0

plt.tick_params(
    axis='both',        # changes apply to both axes
    which='both',       # both major and minor ticks are affected
    bottom=False,       # ticks along the bottom edge are off
    left=False,
    right=False,
    top=False           # ticks along the top edge are off
    )


# rotate axis labels
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

plt.show()